Manipulation of a respiratory model via adjustment of parameters associated with model images

ABSTRACT

A method and apparatus for manipulation of a respiratory model via adjustment of parameters associated with model images is described. The method includes identifying one of more images of a plurality of images that are used with a previously generated model associated with a position and motion of a targeted region of a patient to receive radiation treatment. The method also includes generating, by a processing device, a new model to be associated with the position and motion of the targeted region based on a selection that is associated with one of the one or more images of the plurality of images, wherein the new model is a relationship between a series of internal features and external marker positions. The method further includes delivering radiation to the targeted region based on the new model.

REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/971,694, filed May 4, 2018, which is a continuation of U.S. patentapplication Ser. No. 15/005,971, filed Jan. 25, 2016, now U.S. Pat. No.9,990,711, issued Jun. 5, 2018, which are hereby incorporated byreference in its entirety.

TECHNICAL FIELD

Aspects of the present disclosure relate to the manipulation of arespiratory model via adjustment of parameters associated with modelimages.

BACKGROUND

Reference images of a patient may be used to indicate the position of atarget region of the patient during a radiation treatment procedure. Forconvenience, the term “radiation treatment” is used herein to meanradiosurgery and/or radiotherapy unless otherwise noted. Tracking of thetreatment target increases the accuracy of the radiation treatmentprocedure so that irradiation of the healthy tissue surrounding thetargeted region may be minimized.

A workflow to provide the radiation treatment to a patient may involvemultiple stages corresponding to treatment planning, patient setup, andtreatment delivery as described with regards to FIG. 1. As shown, themethod 100 may begin with the treatment planning as a first stage toprovide radiation treatment to the patient (block 110). The treatmentplanning stage may be initiated by obtaining of pre-treatment diagnosticimages with one or more imaging modalities (e.g., CT image, MR image,etc.) of a volume of interest (VOI) of the patient. The treatmentplanning stage may further include identifying one or more referencepoints in one or more of the pre-treatment images. The reference pointsmay be one or more imageable landmarks or points of interest in theacquired images that can be tracked during later stages discussed below.The acquired images in the treatment planning stage such as a CT imageincludes a pathological anatomy that is targeted for treatment, and wellas a critical region(s) that is positioned near the pathologicalanatomy. Treatment planning software enables the generation of acritical region contour around the critical region and a target regioncontour around the pathological anatomy. Conventionally, a user manuallydelineates or uses a software tool to auto-delineate points on a displaythat is used by the treatment planning software to generate thecorresponding contours. After the target has been defined, the criticaland soft tissue volumes have been specified, and minimum radiation doseto the target and the maximum dose to normal and critical healthy tissuehas been specified, the treatment planning software then produces atreatment plan, relying on the positional capabilities of the radiationtreatment system.

The method 100 may subsequently include a patient setup as a secondstage of the workflow before providing the radiation treatment to thepatient (block 120). A stereo image may be generated, such as by X-rayimaging, or a 3D alignment image may be generated, such as a cone-beamCT (CBCT) or a megavoltage CT (MVCT) image, and then correlated to thepreoperative image in order to locate the target region accurately.Then, a radiation source located on treatment delivery system isautomatically positioned based on the correlation between thepreoperative image and the stereo images (or 3D alignment image) inorder to accurately target the desired treatment region in the patient.If the patient is not within the desired range of the radiationtreatment delivery system, the position of the patient adjusted duringthe patient setup stage.

After the patient setup stage, treatment delivery may be performed onthe patient based on the treatment plan (block 130). The images(s) takenduring the patient set up stage may be used as a delivery reference forlater registration. During treatment delivery, dynamic tracking of thetarget may be performed based on the use of x-ray images taken toidentify internal features in the patient and external markers to trackmotions of the target due to, for example, patient respiration, with theregistration results between a digitally reconstructed radiograph (DRR)and each of the live x-ray images used to generate a correlation model.The external markers may be light emitting diodes (LEDs) that arecoupled to the patient and a tracker or motion detection system to trackthe position of one or more of the external markers. An example of onesuch system is the Synchrony™ respiratory tracking system developed byAccuray, Inc. However, other respiratory tracking systems may be used.After the correlation model is generated, the position measurements ofthe external markers may be used to compute the corresponding locationof the target by using the correlation model. Once the location of thetarget (e.g., the tumor) has been computed, the radiation beam sourceposition of the radiation treatment delivery system may be adjusted tocompensate for the dynamic motion of the target due to patientrespiration (or other movement). The radiation treatment delivery systemmay then deliver the dose of radiation to the tracked target inaccordance with the radiation treatment plan developed during thetreatment planning stage.

Thus, a sequence of x-ray images of a patient may be acquired and acorrespondence between a location of a tumor of the patient and themotion of the patient as represented by LED markers that are placed onthe patient's body may be determined. After the model has beengenerated, the motion of the LED markers may be used to predict thelocation of the tumor. Such information may be used to dynamicallyupdate the delivery of the radiation treatment from the radiationtreatment equipment to the patient so that the target is irradiatedaccording to the treatment plan, even as the location of the targetmoves based on the motion of the patient.

The x-ray images may be obtained based on sequential acquisition of thex-ray images of the patient. Each x-ray image may be correlated with aDRR image as it is acquired and a determination may be made as towhether the correlation results of the x-ray image satisfy correlationcriteria. If the x-ray image satisfies the correlation criteria, thenthe x-ray image may be used in the building of the correlation model.However, if the x-ray image doesn't satisfy the correlation criteria,then the correlation parameters of the x-ray image may be modified orthe x-ray image may not be used in the building of the correlationmodel. Subsequently, another x-ray image may be acquired and the processmay repeat. Thus, a user viewing the acquired x-ray images that are usedto build the correlation model may only view or modify the most recentlyacquired x-ray image.

Furthermore, when viewing the most recently acquired x-ray image thathas been used to build the correlation model, the visibility of a tumorwithin the x-ray image may be difficult when separately viewing the mostrecently acquired x-ray image. For example, to a user reviewing a singlex-ray image, the boundaries, shape, and size of the tumor may bedifficult to identify.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousimplementations of the disclosure.

FIG. 1 is a flow diagram an example method to provide radiationtreatment to a patient in accordance with some embodiments of thepresent disclosure.

FIG. 2 illustrates an example of an image-guided radiation treatmentsystem in accordance with some embodiments of the present disclosure.

FIG. 3 is a flow diagram of an example method to use a model based on aselection of an image in a group of images in accordance with someembodiments of the present disclosure.

FIG. 4 illustrates a table associated with a group of images inaccordance with some embodiments.

FIG. 5 is a flow diagram of an example method to include an image thatwas not successfully correlated in a group of images to be used in amodel in accordance with some embodiments of the present disclosure.

FIG. 6A illustrates an example of a group of images that are used in amodel in accordance with some embodiments.

FIG. 6B illustrates an example of the group of images with amodification in accordance with some embodiments of the presentdisclosure.

FIG. 7 illustrates a portion of an example graphical user interface inaccordance with some embodiments of the present disclosure.

FIG. 8 illustrates another portion of an example graphical userinterface in accordance with some embodiments of the present disclosure.

FIG. 9 illustrates an example method to provide a series of images witha superimposed contour in accordance with some embodiments.

FIG. 10 illustrates the sorting of images to be in the series of imagesbased on amplitude and phase associated with respiration of a patient inaccordance with some embodiments of the present disclosure.

FIG. 11 is a flow diagram of an example method to provide a series ofimages based on a modification to an image in the series of images inaccordance with some embodiments.

FIG. 12A illustrates an example of a moving of the contour during aplaying of the group of images in a movie mode in accordance with someembodiments.

FIG. 12B illustrates an example of the moving of the contour during theplaying of the group of images in a movie mode that may be associatedwith an error in accordance with some embodiments.

FIG. 12C illustrates another example of the moving of the contour duringthe playing of the group of images in a movie mode that may beassociated with another error in accordance with some embodiments.

FIG. 13 illustrates a system that may be used in the generating of atreatment plan and the performing of radiation treatment in accordancewith some embodiments of the present disclosure.

FIG. 14 illustrates a block diagram of an embodiment of a computersystem in which some embodiments of the disclosure may operate.

DETAILED DESCRIPTION

Aspects of the present disclosure are directed to the manipulation of arespiratory model via adjustment of parameters associated with modelimages. In general, a radiation treatment procedure may target a regionof a patient by providing radiation treatment focused to the targetedregion. The model may be used to specify the targeted region during theoperation of radiation treatment equipment that provides the radiationtreatment to the targeted region.

The model used in the radiation treatment procedure may be based on agroup of images of the patient. For example, the series of images may bebased on pre-operative x-ray images of the patient. The x-ray images mayinclude a region that is intended to be targeted by the radiationtreatment (e.g., a tumor) and a region that is not intended to betargeted by the radiation treatment (e.g., surrounding healthy tissue).The model may be used to specify the targeted region for the radiationtreatment provided by the radiation treatment equipment. In general, themodel may be generated by a correlation process that identifies anobject, such as the tumor, in the x-ray images. In one embodiment, thecorrelation process may be based on Xsight™ Lung, Xsight™ Spine, andFiducial Tracking processes developed by Accuray, Inc. In alternativeembodiments, other correlation processes may be used. The results ofsuch a correlation process may be used to control the radiationtreatment equipment. As such, the model may be generated using a seriesof x-ray images and may be used to control the providing of theradiation treatment to the targeted region of the patient. In someembodiments, a contour corresponding to the targeted region recognizedby the correlation process may be superimposed upon the series of x-rayimages to provide an indication of the targeted region on the x-rayimages. Further details with regard to the contour are described below.

As such, the model may be based on a series of x-ray images. The x-rayimages may be sequentially added to the model by the user creating themodel. For example, the model may be based on a threshold or maximumnumber of images (e.g., fifteen x-ray images) and each x-ray image maybe added to the model one at a time. For example, a first x-ray imagemay be provided to the user or viewer who may select to include thefirst x-ray image as one of the images in the model. After the user orviewer has provided a selection for the first x-ray image, then a secondx-ray image may be provided to the user or viewer. If the user or viewerhas selected to include fifteen x-ray images (e.g., the threshold ormaximum number of images) in the model, but later decides to change themodel by removing the seventh x-ray image of the fifteen x-ray images,then the user may begin to add additional x-ray images to be included inthe model until the seventh x-ray image that the user intends to removehas been pushed out of the model. For example, the model may includefifteen x-ray images as a maximum number of x-ray images that may beincluded in the model. If the user seeks to remove the seventh x-rayimage, then eight new x-ray images may be added to the model so that theseventh x-ray is pushed out of the model along with the eighth throughfifteenth x-ray images and are replaced by other x-ray images.

Instead of removing the x-ray images that are used in the model byadding additional x-ray images to push out particular x-ray images, thex-ray images may be provided to the user or viewer as a group of imagesthat may each be individually removed or added for use in the model. Forexample, instead of removing the seventh x-ray image in a series ofimages by adding new x-ray images to the model, the user or viewer maybe provided a graphical user interface (GUI) that provides an option toremove or add any of the x-ray images for use in the model. In someembodiments, the GUI may provide a table that identifies each of thex-ray images that may be used in generation of the model. The table mayidentify, for each of the x-ray images, whether the x-ray image hasalready been included to be used in the generation of the model orwhether the x-ray image has not been included to be used in thegeneration of the model, and other information related to each of thex-ray images. Furthermore, an entry of the table that corresponds to aparticular x-ray image may be selected to provide a visual display ofthe x-ray image and a contour that is superimposed on the x-ray imagewhere the contour represents where the radiation treatment will beprovided (e.g., where the tumor is recognized by the correlation processrelative to the x-ray image). As such, the GUI may provide the user orviewer to operate upon the x-ray images as a group or an ensemble asopposed to individual x-ray images in a sequential manner. Thus, theuser or viewer may modify the model more easily by operating onparameters associated with each x-ray image in any order as opposed tobeing provided a sequential sequence of x-ray images.

The GUI may further provide or play the group of x-ray images that areincluded in the generation of the model in a sequence so that the tumorin the x-ray images may be more easily identifiable (i.e., a moviemode). For example, the x-ray images may be sorted based on arespiratory order of the patient. The playing or providing of the x-rayimages that are sorted based on respiratory order in the movie mode mayallow a user or viewer to more easily ascertain the location of thetumor around healthy tissue and bone structure during respiration of thepatient. Furthermore, the contour that represents the targeted regionfor the radiation treatment may be superimposed on each x-ray image asthe sorted x-ray images are played or provided to the user or viewer.Since the contour represents the targeted region of the radiationtreatment, the user or viewer may verify that the contour contains thetumor in each x-ray image and may thus verify that the radiationtreatment is targeting the correct region of the patient (e.g., thetumor and not healthy tissue) during the range of motion of respirationof the patient.

If the user or viewer identifies that the contour for at least one ofthe x-ray images is not encompassing the tumor from the x-ray image,then the user or viewer may modify or remove the x-ray image since theradiation treatment may not be correctly targeting the tumor based onthe x-ray image. For example, as previously described, the user orviewer may use the GUI to remove the x-ray image from the group ofimages that are used to identify the targeted region. In someembodiments, the user or viewer may change parameters associated withthe correlation process that is used to correlate the x-ray image withthe targeted region. For example, correlation parameter thresholds maybe adjusted so that x-ray images that were not previously consideredsuccessful may be considered successful based on the adjusted or newcorrelation parameter threshold. Furthermore, correlation parameterinputs may also be adjusted and the x-ray images may be re-correlated toproduce successful results in response to the adjusted or newcorrelation parameter inputs. Although aspects of the present disclosurerefer to a correlation process, other types of target location processesmay be used. For example, other target location processes that mayperform a computation of a similarity measure may be used. Such targetlocation processes may include, but are not limited to,cross-correlation, normalized cross-correlation, pattern intensity,mutual information, normalized mutual information, mean squareddifference, mean absolute difference, etc. Thus, embodiments of thepresent disclosure may alternatively use any type of target locationprocess.

Accordingly, aspects of the present disclosure may allow a user orviewer to verify that the targeted region of the radiation treatment iscorrectly identified. If one of the x-ray images does not correctlytarget a tumor (e.g., the contour is not over the tumor and thus thecorrelation of the x-ray image is an error), then the user or viewer mayuse the GUI to either remove the x-ray image or to change parametersassociated with the correlation process so that the targeted region ofthe radiation treatment contains the tumor instead of healthy tissue ofthe patient.

FIG. 2 illustrates an example of an image-guided radiation treatmentsystem 200. In general, the image-guided radiation treatment system 200may provide radiation treatment to a targeted region of a patient basedon a treatment plan from correlation results that are reviewed via theGUI 700 or 800 of FIG. 7 or 8.

As shown, FIG. 2 illustrates a configuration of an image-guidedradiation treatment system 200. In the illustrated embodiments, theradiation treatment system 200 includes a linear accelerator (LINAC) 201that acts as a radiation treatment source. In one embodiment, the LINAC201 is mounted on the end of a robotic arm 202 having multiple (e.g., 5or more) degrees of freedom in order to position the LINAC 201 toirradiate a pathological anatomy (e.g., target 220) with beams deliveredfrom many angles, in many planes, in an operating volume around apatient. Treatment may involve beam paths with a single isocenter,multiple isocenters, or with a non-isocentric approach. Alternatively,other types of image guided radiation treatment (IGRT) systems may beused. In one alternative embodiment, the LINAC 201 may be mounted on agantry based system to provide isocentric beam paths.

In one embodiment, the LINAC 201 may be positioned at multiple differentnodes (predefined positions at which the LINAC 201 is stopped andradiation may be delivered) during treatment by moving the robotic arm235. At the nodes, the LINAC 201 can deliver one or more radiationtreatment beams to a target. The nodes may be arranged in anapproximately spherical distribution about a patient. The particularnumber of nodes and the number of treatment beams applied at each nodemay vary as a function of the location and type of pathological anatomyto be treated.

Referring to FIG. 2, the image-guided radiation treatment system 200 mayinclude an imaging system 265 having a processor 230 connected withx-ray sources 203A and 203B and fixed x-ray detectors 204A and 204B.Alternatively, the x-ray sources 203A, 203B and/or x-ray detectors 204A,204B may be mobile, in which case they may be repositioned to maintainalignment with the target 220, or alternatively to image the target fromdifferent orientations or to acquire many x-ray images and reconstruct athree-dimensional (3D) cone-beam CT. In one embodiment, the x-raysources are not point sources, but rather x-ray source arrays, as wouldbe appreciated by the skilled artisan. In one embodiment, LINAC 201serves as an imaging source, where the LINAC power level is reduced toacceptable levels for imaging.

Imaging system 265 may perform computed tomography (CT) such as conebeam CT or helical megavoltage computed tomography (MVCT), and imagesgenerated by imaging system 265 may be two-dimensional (2D) orthree-dimensional (3D). The two x-ray sources 203A and 203B may bemounted in fixed positions on the ceiling of an operating room and maybe aligned to project x-ray imaging beams from two different angularpositions (e.g., separated by 90 degrees) to intersect at a machineisocenter (referred to herein as a treatment center, which provides areference point for positioning the patient on a treatment couch 206during treatment) and to illuminate imaging planes of respectivedetectors 204A and 204B after passing through the patient. In oneembodiment, imaging system 265 provides stereoscopic imaging of thetarget 220 and the surrounding volume of interest (VOI). In otherembodiments, imaging system 265 may include more or less than two x-raysources and more or less than two detectors, and any of the detectorsmay be movable rather than fixed. In yet other embodiments, thepositions of the x-ray sources and the detectors may be interchanged.Detectors 204A and 204B may be fabricated from a scintillating materialthat converts the x-rays to visible light (e.g., amorphous silicon), andan array of CMOS (complementary metal oxide silicon) or CCD(charge-coupled device) imaging cells that convert the light to adigital image that can be compared with a reference image during animage registration process that transforms a coordinate system of thedigital image to a coordinate system of the reference image, as is wellknown to the skilled artisan. The reference image may be, for example, adigitally reconstructed radiograph (DRR), which is a virtual x-ray imagethat is generated from a 3D CT image based on simulating the x-ray imageformation process by casting rays through the CT image.

The image-guided radiation treatment 200 may include a motion detectiondevice 214 to determine target motion within a detection field. Themotion detecting device 214 may detect external patient motion (such aschest movement during respiration) that occurs within an area of thepatient 225. The motion detecting device 214 can be any sensor or otherdevice capable of identifying target movement. The motion detectingdevice 214 may be an optical sensor such as a camera, a pressure sensor,an electromagnetic sensor, or some other sensor that may provide motiondetection without delivering ionizing radiation to a user (e.g., asensor other than an x-ray imaging system). In one embodiment, themotion detecting device 214 acquires measurement data indicative oftarget motion in real-time. Alternatively, the measurement data may beacquired at a frequency that is higher (than can be achieved or than isdesirable with x-ray imaging (due to ionizing radiation delivered to thepatient with each x-ray image). In one embodiment, the motion detectingdevice 214 does not provide high absolute position accuracy. Instead,the motion detecting device 214 may provide sufficient relative positionaccuracy to detect patient movement and/or target movement.

In one embodiment, the motion detecting device 214 is an optical system,such as a camera. The optical system may track the position oflight-emitting diodes (LEDs) 241 situated on patient 225. Alternatively,the optical system may directly track a surface region of patient 225,as distinguished from tracking LEDs 241 on the patient. There may be acorrelation between movement of the target and movement of the LEDs 241and/or surface region of the patient 225. Based on the correlation, whenmotion of the LEDs 241 and/or surface region is detected, it can bedetermined that the target 220 has also moved sufficiently to requireanother diagnostic x-ray image to precisely determine the location ofthe target.

As shown in FIG. 2, the image-guided radiation treatment system 200 mayfurther be associated with a treatment delivery workstation 250. Thetreatment delivery workstation may be remotely located from theimage-guided radiation treatment system 200 in a different room that thetreatment room in which the system 200 and patient are located. Thetreatment delivery workstation 250 may include a processing device andmemory that implements a model controller 1499 that provides a first GUI(e.g., GUI 700 of FIG. 7) and a second GUI (e.g., GUI 800 of FIG. 8) todisplay the x-ray images acquired by the imaging system 265. The firstGUI may identify the x-ray images that are used to identify the target220 based on a respiratory order of the patient 225 and the second GUImay identify images acquired by the imaging system 265 that are bothused to identify the target 220 and that are not used to identify thetarget 220.

In some embodiments, a gantry system with a helical delivery may be usedto rotate the imaging system 265. For example, the gantry system may beused to acquire two, three, or more images (e.g., x-ray images) atdifferent angles that may be provided to a user in a GUI as described infurther detail in conjunction with FIG. 7.

FIG. 3 is a flow diagram an example method to generate a model based ona selection of an image in a group of images. In general, the method 300may be performed by processing logic that may comprise hardware (e.g.,processing device, circuitry, dedicated logic, programmable logic,microcode, hardware of a device, integrated circuit, etc.), software(e.g., instructions run or executed on a processing device), or acombination thereof. In some embodiments, the method 300 is provided viathe graphical user interface (GUI) 700 or 800 of FIGS. 7 and 8.

As shown in FIG. 3, the method 300 may begin with the processing logicreceiving a group of images associated with a patient (block 310). Forexample, images of a first modality representing a patient may bereceived. In some embodiments, the images may correspond to x-ray imagesof a region of a patient. In the same or alternative embodiments, pairsof x-ray images of the region of the patient may be received. Theprocessing logic may further identify images of the group of images thatare used in a model that is used to target a region of the patient(block 320). For example, each of the identified x-ray images may beused to identify the targeted region of the patient that will receive aradiation treatment for the targeted region. In some embodiments, theimages that are used in the generation of the model may be x-ray imagesthat satisfy correlation parameter thresholds. The processing logic maysubsequently sort the images that are used to identify the targetedregion based on a respiratory motion of the patient (block 325). Forexample, each of the x-ray images may be sorted based on a positionwithin the respiratory motion of the patient. A first position in therespiratory motion may correspond to the beginning point of therespiratory motion of the patient and a last position in the respiratorymotion may correspond to the final point of the respiratory motion ofthe patient. An intermediate position may correspond to a middle pointthat is between the beginning point and the final point of therespiratory motion of the patient. Further details with regard tosorting images based on respiratory motion are described in conjunctionwith FIG. 10.

Referring to FIG. 3, the processing logic may further identify imagesfrom the group of images that are not used in the generation of themodel that is used to target the region of the patient (block 330). Forexample, x-ray images that do not satisfy the correlation parameterthresholds may be identified. The processing logic may further receive amodification selection associated with one of the sorted images that isat an intermediate position in the respiratory order (block 340). Themodification selection may correspond to a removal of one of the imagesor a modification of a parameter associated with one of the images thatare used in the generation of the model. For example, the selection mayremove an x-ray image that is at the intermediate position and iscurrently used in the generation of the model or may correspond to achanging of a correlation parameter threshold that may be used toindicate whether or not an x-ray image is successfully or notsuccessfully correlated. The modification selection may further be achange to a correlation parameter input where a change in thecorrelation parameter input may result in a re-correlation of images asdescribed in further detail below. A GUI that may be used to provide themodification selection associated with the group of images as describedwith regard to FIGS. 7 and 8. The processing logic may further generatea new model or may perform a new correlation process based on themodification selection (block 350). For example, a new model may begenerated based on a removing of an x-ray image at an intermediateposition and that was used in the generating of the previous model, achanging of a correlation parameter threshold associated with the groupof images that may identify which of the x-ray images in the group ofimages satisfy the changed correlation parameter threshold and which ofthe x-ray images do not satisfy the changed correlation parameterthreshold, or a changing of a correlation parameter input which mayperform a re-correlation of the x-ray images.

Furthermore, in some embodiments, the modification selection maycorrespond to including an x-ray image to be used in the generation ofthe model when the x-ray image was not previously used in the generationof the model. Thus, a new model may further be generated based on theadding of an x-ray image that was previously identified as not beingused in the model. The added x-ray may further be at an intermediateposition relative to the respiratory order or motion of the patient.

As such, a user may be provided with a GUI that provides an indicationof each of the images that are used in the generation of a model toprovide radiation treatment to a targeted region of a patient as well asan indication of each of the images that are not used in the generationof the model. A selection may be made via the GUI of one of the imagesto modify the image or to change correlation parameter thresholds orcorrelation parameter inputs associated with the correlation processthat has analyzed the group of images. The modification may be to add,remove, or modify parameters. associated with the model.

FIG. 4 illustrates a table 400 associated with a group of images. Ingeneral, the group of images may be associated with a model that is usedto provide radiation treatment to a targeted region of a patient. Thetable 400 may be provided in a GUI 800 as described in conjunction withFIG. 8.

As shown in FIG. 4, the table 400 may include multiple entries or rowswhere each row or entry may correspond to an image (e.g., an x-rayimage) in an image history of a patient. For example, a first row orentry 410 may correspond to a first x-ray image and a second row orentry 420 may correspond to a second x-ray image. In some embodiments,the table 400 may identify whether an x-ray image is currently beingused in a model to identify a target region of a patient or whether thex-ray image is not being used in the model to identify the targetregion. For example, the table 400 may include a column 430 thatidentifies whether a corresponding image is currently being used by themodel (e.g., represented by a checkmark) and whether the correspondingimage is not currently being used in the model (e.g., represented by an‘X’). As shown, the row or entry 410 may identify that the first imageis being used in the model and the row or entry 420 may identify thatthe second image is not currently being used in the model.

Furthermore, a column 440 may identify a model point associated witheach of the x-ray images. Furthermore, a column 450 may identify a phaseof each corresponding x-ray image relative to a respiratory orderassociated with the patient. For example, the column 450 may identify aposition of the corresponding x-ray image after the images have beensorted based on respiratory order of the patient. The position may beillustrated by a marker (e.g., a circle or a dot) that identifies thepoint in the respiratory phase (e.g., a peak, a valley, rising middle,falling middle, etc.) of the patient when the x-ray image was acquired.Further details with regard to sorting of the x-ray images based onrespiratory order are described in conjunction with FIG. 10.Furthermore, the table 400 may include a column 460 that may identify atime associated with the age of each corresponding x-ray image. Forexample, the table 400 may identify images of a patient that were takenduring a particular time period (e.g., thirty seconds) and may identifythe age of each of the images relative to the current time. In someembodiments, the times identified in the column 460 may be dynamic andupdated as time progresses. Furthermore, the table 400 may be sortedbased on any of the columns 430, 440, 450, and 460. For example, therows of the table 400 may be provided in a first order based on aselection of the column 460 (e.g., based on ascending or descendingorder of the age of each image) or a second order based on a selectionof the column 430 so that images included in the model are provided inthe table above or below the images that are not included in the model.

As such, the table 400 may indicate whether each x-ray image is used oris not used in the identification of a targeted region of a patient aswell as additional information associated with each of the images.Further details with regard to a GUI that may be provided to a user or aviewer with regard to x-ray images used to generate the model aredescribed in conjunction with FIGS. 7 and 8.

FIG. 5 is a flow diagram of an example method 500 to include an imagethat was not successfully correlated in a group of images to generate amodel. In general, the method 500 may be performed by processing logicthat may comprise hardware (e.g., processing device, circuitry,dedicated logic, programmable logic, microcode, hardware of a device,integrated circuit, etc.), software (e.g., instructions run or executedon a processing device), or a combination thereof. In some embodiments,the method 500 is provided via the graphical user interface 700 or 800of FIGS. 7 and 8.

As shown in FIG. 5, the method 500 may begin with the processing logicreceiving an image (block 510). For example, an x-ray image of a patientmay be received. The processing logic may determine whether the imagehas been successfully correlated (block 520). For example, adetermination may be made as to whether one or more correlationparameters that is provided by a correlation (or image registration ortarget localization) process of the x-ray image satisfy correlationparameter thresholds. If the image was successfully correlated (e.g.,the correlation parameters satisfy the correlation parameterthresholds), then the x-ray image may be used to generate the model(block 530). Furthermore, a GUI may provide an indication or identifythat the image has been used to generate the model for identifying atargeted region of the patient and/or has been successfully correlated.However, if the x-ray image was not successfully correlated (e.g., thecorrelation parameters for the x-ray image do not satisfy thecorrelation parameter thresholds), then the processing logic may not usethe x-ray image to generate the model (block 540). For example, thex-ray image may not be used in the identification of a targeted regionof the patient and the GUI may provide an indication or identify thatthe image has not been used to generate the model and/or was notsuccessfully correlated. In some embodiments, an image may not besuccessfully correlated when the correlation process fails to localizethe targeted region (e.g., a confidence metric or parameter is low ordoes not satisfy a threshold value) or by an input provided by a user.Furthermore, the processing logic may receive a selection to change acorrelation parameter input or a correlation parameter threshold (block550). Subsequently, the processing logic may determine if the image isnow successfully correlated after the change to the correlationparameter input or the correlation parameter threshold (block 560). Inresponse to determining that the image is now successfully correlated,the processing logic may provide an indication that the image is nowsuccessfully correlated and use image in the model (block 570) or inresponse to determining that the image is still not successfullycorrelated, the processing logic may provide an indication that theimage is still not successfully correlated and may not use the image inthe model (block 580). For example, the correlation parameter thresholdsfor the correlation process that is used to correlate the group of x-rayimages may be changed and the x-ray image that was previously notsuccessfully correlated may be considered to be successfully correlatedif the correlation parameters of the image satisfy the updatedcorrelation parameter thresholds. Subsequently, a new model based on thex-ray image that was not included in the previous model may be generatedor used by the model to identify the targeted region.

FIG. 6A illustrates an example of a group of images 600 that are used togenerate a model. As shown in FIG. 6A, the group of images 600 mayinclude a first x-ray image 610, a second x-ray image 620, third x-rayimage 630, fourth x-ray image 640, and a fifth x-ray image 650. In someembodiments, each image may correspond to an x-ray image. At a firsttime, the first x-ray image 610, third x-ray image 630, and fourth x-rayimage 640 may be used in the generation of a model that is used toidentify a targeted region for receiving radiation treatment. Forexample, the first, third, and fourth x-ray images 610, 630, and 640 maybe associated with correlation parameters that satisfy correlationparameter thresholds. As such, the first x-ray image 610, third x-rayimage 630, and fourth x-ray image 640 may be selected to be used in thegeneration of a model 660. However, the second x-ray image 620 and fifthx-ray image 650 may not be associated with correlation parameters thatsatisfy correlation parameter thresholds. As such, the second x-rayimage 620 and the fifth x-ray image 650 may not be selected to be usedin the generation of the model 660.

FIG. 6B illustrates an example of the group of images 600 with amodification. In general, at a second time that is after the first timewhen the first x-ray image 610, third x-ray image 630, and fourth x-rayimage 640 satisfied the correlation parameter thresholds and were usedin the generation of the model, additional images may be used in thegeneration of a subsequent model. For example, a user may use agraphical user interface, as described in further detail with regard toFIGS. 7 and 8, to modify correlation parameter thresholds for the groupof images 600. In response to the modifying of the correlation parameterthresholds, a determination may be made as to whether the correlationparameters for each of the x-ray images 610, 620, 630, 640, and 650satisfy the new correlation parameter thresholds. For example, themodification to the correlation parameter thresholds may decreaserequirements for a correlation parameter of a particular x-ray image. Assuch, while the correlation parameters associated with the second x-rayimage 620 and fifth x-ray image 650 did not satisfy the previouscorrelation parameter thresholds, the correlation parameters of thesecond and fifth images 620 and 650 may satisfy the correlationparameter thresholds after being modified by the user to decrease therequirements of the correlation parameter thresholds. As such, a usermay subsequently select the second x-ray image 620 and the fifth x-rayimage 650 to be included in the generation of a new model 670.

As such, at a first time, a determination may be made as to whether eachimage of a group of images is associated with one or more correlationparameters that satisfy one or more correlation parameter thresholds. Ifthe images satisfy the one or more correlation parameter thresholds,then an indication may be provided that the respective images aresuccessfully correlated and the images may be selected to be used in amodel. At a second time, updated correlation parameter thresholds may beprovided and a second determination may be made as to whether each imagein the group of images is associated with one or more correlationparameters that satisfy one or more updated correlation parameterthresholds. Additional images or fewer images may subsequently beidentified as being associated with correlation parameters that satisfythe updated correlation parameter thresholds at the second time. Thus,the selection of images that are used to generate the model may bechanged or modified based on the updated correlation parameterthresholds.

FIG. 7 illustrates a portion of an example graphical user interface(GUI) 700. In general, the GUI 700 may be used to modify a selection ofa group of images used to generate a model to provide radiationtreatment to a targeted region of a patient as described with regard toFIGS. 1 and 2. The GUI 700 may refer to the targeted regioncorresponding to a lung tumor, but the GUI 700 may be used withidentifying target regions based on fiducial markers and spine tumors.

As shown in FIG. 7, the GUI 700 may include a pair of x-ray images thatincludes a first x-ray image 701 and a second x-ray image 702. In someembodiments, the first x-ray image 701 and the second x-ray image 702may correspond to x-ray images of the patient from two different angles.For example, the first x-ray image 701 may be taken from a first angle(e.g., a forty five degree angle) and the second image 702 may be takenfrom a second angle (e.g., a one hundred thirty five degree angle). Eachof the x-ray images 701 and 702 may include an identified tumor of thepatient. Thus, each of the first and second x-ray images 701 and 702 mayrepresent images of a tumor and surrounding healthy tissue and bonestructure of the patient from different sides. Furthermore, each of thefirst x-ray image 701 and the second x-ray image 702 may include acontour 708 that represents the targeted region of the patient asidentified by the correlation process as previously described. In someembodiments, if fiducial marker tracking is implemented, then fiducialmarker graphics may be superimposed on each of the x-ray images insteadof the contours. The fiducial marker may indicate a location for which afiducial may have been identified.

The GUI 700 may further include a table 703 that corresponds tocandidate images that may be included in the model that is used toidentify the targeted region of the patient. The table 703 may identifyeach x-ray image from multiple x-ray images that are available to beselected to be used by the model to identify the targeted region. Insome embodiments, the table 703 may include a column 704 that identifiesan amount of error for each corresponding x-ray image. The error may bebased on a 3D distance (e.g., in millimeters) of a point of the x-rayimage from a fitted motion model. For example, if the fitted motionmodel is a linear model, the error may be based on the distance betweenthe location of an object (e.g., a tumor) as identified on the pair ofx-ray images and the nearest point on the linear motion model. If theobject moves across a straight line, then the error may be at a value of‘0.’ However, if the object does not move across a straight line, thenthe error may be at a value that is larger than ‘0.’ Furthermore, thecolumn 705 may indicate an offset in a particular axis (e.g., anx-axis), the column 706 may indicate another offset in another axis(e.g., the y-axis), and the column 707 may indicate another offset in athird axis (e.g., the z-axis). Each of the columns 706-708 may indicatea distance of the tumor that is identified in the x-ray image withrespect to the location of the tumor image in the DRR. For example, fora particular pair of x-ray images, the column 705 may indicate how farthe identified tumor is relative to the tumor image in the DRR relativeto the x-axis of each of the pair of x-ray images. Thus, the table 703may indicate an error and offsets for each pair of x-ray images that isselected to be used in the model to identify a targeted region of apatient.

The GUI 700 may further include correlation parameter inputscorresponding to a first tracking range selection 709 and a secondtracking range selection 710. Each of the first tracking range selection709 and the second tracking range selection 710 may specify a distancein each axis that may be searched in the x-ray images to identify thetargeted region. In general, if a tracking range is too small, then thetargeted region (e.g., the tumor) may not be detected once the tumorleaves the tracking range and if the tracking range is too large, thencomputation times may be excessive and other objects may be erroneouslyidentified as a match for the targeted region. As such, the firsttracking range selection 709 may correspond to a tracking range in anx-axis for a correlation process that identifies the targeted region ofa patient from the x-ray image and the second tracking range selection710 may correspond to a tracking range in a y-axis for the correlationprocess that identifies the targeted region of the patient. The firstand second tracking range selections 709 and 710 may be inputs orparameters to be used by the correlation process (i.e., correlationparameter inputs). For example, a user of the GUI 700 may provide orselect a new value for one or both of the first tracking range selection709 and the second tracking range selection 710 and the correlationprocess for each of the x-ray images of the model (e.g., as representedin the table 703) may be recomputed (i.e., the x-ray images arere-correlated) to determine whether each of the x-ray images currentlyused in the model are associated with new correlation parameters resultsthat satisfy correlation parameter thresholds after the re-correlatingof the x-ray images. For example, the first correlation parameterthreshold for the result ‘dxAB’ may indicate, for each of the x-rayimages, whether the corresponding correlation parameter result 712satisfies a respective correlation parameter threshold 714 for dxAB andthe second correlation parameter threshold 713 may indicate whether thesecond correlation parameter result 713 for Uncertainty Percentageassociated with the x-ray image satisfies another correlation parameterthreshold 715. In some embodiments, the first correlation parameterresult ‘dxAB’ may be a detection quality metric that may be used toindicate an inconsistent result. For example, as previously mentioned,pairs of x-ray images of the patient may be acquired. If the object, ortumor, is identified in both of the x-ray images, then the position ofthe tumor along the x-axis may be equal in each of the x-ray images. Thefirst correlation parameter result for dxAB′ may provide a valuecorresponding to any difference of the position of the tumor along thex-axis of the pair of x-ray images. A large difference in distance mayindicate a failed detection. Furthermore, the second correlationparameter result 713 may correspond to a detection confidence metric. Insome embodiments, the detection confidence metric may be based on anumber of local maxima near a global maximum, convexity of the objectivefunction, etc.

As an example, a user of the GUI 700 may change or modify thecorrelation parameter inputs to the correlation process by providing anew value for the first tracking range selection 709 and a new value forthe second tracking range selection 710. In some embodiments, the newvalue may be provided by entering the new value in a text box (e.g., atext box 720) or by using a slider 721 so that moving the slider to theleft may decrease the new value and moving the slider to the right mayincrease the new value. In response to the entering of the new value ornew values, the correlation process may be performed on the x-ray imagesof the table 703 to re-correlate the x-ray images. New correlationparameter results may be determined and a determination may be performedas to which of the x-ray images associated with the new correlationparameter results satisfies correlation parameter thresholds. Forexample, a first correlation parameter result 712 and a secondcorrelation parameter result 713 may be provided for each of the x-rayimages as well as an indication as to whether the correlation parameterresults for each corresponding image satisfies the correlation parameterthresholds 714 and 715. For example, the first correlation parameterresult 712 for dxAB′ and the second correlation parameter result 713 maybe re-calculated for each x-ray image in response to the re-correlationof the x-ray images. If the value of the first correlation parameterresult 712 exceeds a correlation parameter threshold 714, then the firstcorrelation parameter result 712 of the particular x-ray image may notbe considered to satisfy the correlation parameter threshold 714.However, if the value of the first correlation parameter result 712 doesnot exceed the correlation parameter threshold 714, then the firstcorrelation parameter result 712 of the x-ray image may be considered tosatisfy the correlation parameter threshold 714. Similarly, if thesecond correlation parameter result 713 does not exceed the correlationparameter threshold 715, then the second correlation parameter result714 for the x-ray image may be considered to satisfy the correlationparameter 715.

In some embodiments, a selection of one of the rows from table 703 mayprovide the x-ray images corresponding to the row to be displayed (e.g.,at locations of x-rays 701 and 702) and to provide the correlationinformation of the x-ray images associated with the row (e.g., thecorrelation parameters and correlation parameter thresholds).

As such, a group of images may be provided via the GUI where each of theimages may be a candidate image to be included in the model. Correlationparameter inputs that are used by a correlation process may be changedto re-correlate each of the images and the correlation parameter resultsmay be determined by the correlation process with the new inputs. Anindication may be provided as to whether each of the images with the newcorrelation parameter results satisfies the correlation parameterthresholds. Furthermore, correlation parameter thresholds may be changedand a new determination may be made as to whether current correlationparameter results of each of the images satisfies the new correlationparameter thresholds. Images that satisfy the correlation parameterthresholds may be successful candidates to be included in the modelwhile images that do not satisfy the correlation parameters may not besuccessful candidates to be included in the model.

FIG. 8 illustrates another portion of an example graphical userinterface 800. In general, the GUI 800 may be used to provide additionalinformation to modify the selection of a group of images as described inconjunction with FIG. 7. In some embodiments, the GUI 800 may beprovided with the GUI 700 of FIG. 7. For example, the GUI 700 and theGUI 800 may be simultaneously displayed or provided to a user or viewer.

As shown in FIG. 8, the GUI 800 may include a table 810 that includesmultiple x-ray images, or pairs of x-ray images, of the patient.Furthermore, the table 810 may be referred to as an image history of thepatient over a particular time period. For example, the table 810 mayinclude multiple images acquired of a patient and one or more of theimages in the table 810 may be selected to be included as a candidateimage for the model to be provided in the table 703. The table 810 mayinclude a column 811 that identifies, for each of the x-ray images, aposition of the x-ray image in a sorted respiratory order of thepatient. Further details with regard to the sorting based on respiratoryorder are described in conjunction with FIG. 10. The column 812 mayidentify the age of each of the x-ray images. For example, the x-rayimages in the table 811 may be all x-ray images that were taken of apatient over a particular time period. The age of each image may berelative to the current point in time and the age may be updated as timeprogresses. Furthermore, the column 813 may indicate whether each of thex-ray images in the table 811 have been selected to be used or have notbeen selected to be used in the model to identify the targeted region ofthe patient. For example, if an x-ray image (or a pair of x-ray images)is selected to be included as a candidate image in the use of the modelto identify the targeted region of the patient (e.g., in response to aselection in the column 813), then the x-ray image (or pair of x-rayimages) may also be identified in the table 703 of the GUI 700. In someembodiments, the table 810 or image history of the patient may includemore x-ray images than the table 703 of the x-ray images that have beenselected for use as candidate images in the model. Thus, a selection toremove one of the x-ray images from the model may be provided by aninput on the selection icon of column 813. Furthermore, the table 810may be sorted based on a value corresponding to a selection of thecolumns 811, 812, and 813. As such, the table 810 may include a subsetof the x-ray images from the table 703. For example, the table 810 mayinclude a proper subset of the x-ray images from the table 703 (e.g.,less x-ray images than the table 703) or may identify every image fromthe table 703.

Thus, the GUI 800 may include a table corresponding to an image historyof the patient. Images from the image history may be selected to beincluded as candidate images for the model that is used to identify thetargeted region of the patient as well as images that were previouslyincluded in the model may be removed from the model so that the image isno longer used in the identifying of the targeted region.

Referring to FIG. 8, the GUI 800 may further include a portion 820 thatmay indicate a position of the patient as represented by the selectedx-ray image relative to one or more markers. For example, a marker(e.g., an LED marker) may be selected or removed from being used in theindication of the position of the portion of the patient relative to theselected x-ray image. The markers may be used to identify a location ofthe x-ray image relative to a respiratory order of the patient. The GUI800 may further include a coronal view graph 830 and a sagittal viewgraph 840 of the x-ray images that are used in the model. For example,the coronal view graph 830 and the sagittal view graph 840 may provide adisplay of a correlation result in a particular plane (e.g., the coronalplane or the sagittal plane of a patient). Each of the coronal viewgraph 830 and the sagittal view graph 840 may display a path associatedwith the targeted region identified by the model and may illustrate apoint on the path for each x-ray image that is included in the model. Insome embodiments, a deviation from an expected path may indicate that acorrelation using a particular x-ray image may be invalid or incorrect(e.g., an error associated with the correlation process when analyzingthe x-ray image) and a user of the GUI 800 may select a portion of thepath or a graphical indicator (e.g., a dot or circle) that is invalid orincorrect and may receive a selection of the corresponding x-ray imagein the table 703 and table 810. The user may subsequently provide aselection in the table 811 to remove the corresponding incorrect x-rayimage from the model as represented by the table 703. For example, afterselecting the portion of the path, the x-ray image may be highlighted inthe GUI at the table 810 and the table 703. A selection may be made inthe table 810 to uncheck and to remove the x-ray image from use in themodel so that the x-ray image is also removed from the table 703.

Referring to FIG. 7, a sequential playing or providing of the x-rayimages or pair of x-ray images (i.e., a movie mode) may be initiated inresponse to a selection of the movie mode icon 730. Further details withregard to the movie mode are described below.

FIG. 9 illustrates an example method 900 to provide a series of imageswith a superimposed contour. In general, the method 900 may be performedby processing logic that may comprise hardware (e.g., processing device,circuitry, dedicated logic, programmable logic, microcode, hardware of adevice, integrated circuit, etc.), software (e.g., instructions run orexecuted on a processing device), or a combination thereof. In someembodiments, the method 900 is provided via the graphical user interface700 or 800 of FIGS. 7 and 8.

As shown in FIG. 9, the method 900 may begin with the processing logicreceiving a group of images (block 910). For example, a number of x-rayimages that each are associated with correlation parameters that satisfycorrelation parameter thresholds and that have been selected by a userto be included in the use by a model to identify a targeted region of apatient may be received. The processing logic may further generate asorting order for the group of images based on a motion of the patient(block 920). In some embodiments, the sorting order may correspond to arespiratory order of the patient. Further details with regard to thesorting based on respiratory order are described in conjunction withFIG. 10. Furthermore, the processing logic may identify a contour oneach of the series of images (block 930). The contour may represent atargeted region of the patient that is intended to receive radiationtreatment. In some embodiments, the contour may be generated earlierduring treatment planning for the patient and the contour that isearlier generated for each image may be included or superimposed on adisplay of the corresponding image. Thus, the contour is superimposed oneach of the x-ray images that are used in the model. In alternativeembodiments, another type of visual indicator may be used instead of thecontour. For example, a crosshair may be used to identify the center ofa detected tracking target or targeted region or a DRR may be overlaidon the x-ray image to show an alignment between the treatment plan andthe x-ray image. Any type of visual indicator aside from a crosshair,contour, or DRR may be used and superimposed on each of the x-rayimages. Subsequently, the processing logic may receive an input toinitiate a playing of the group of images in a movie mode (block 940).For example, a selection from a GUI as described with regard to FIG. 7may initiate a playing of the group of images in the sorted order thatis based on the motion of the patient (i.e., the movie mode of the x-rayimages). Subsequently, the processing logic may provide the group ofimages in a series based on the sorted order that corresponds to themotion of the patient (block 950). In an alternative embodiment, theplaying of the group of images in the sorted order may be doneautomatically without user input.

As such, the x-ray images of a patient that are used by a model toidentify a targeted region of the patient are sorted based on arespiratory order of the patient. The sorted x-ray images may besequentially provided so that a progression of the x-ray imagescommences. As an example, the group of x-ray images may include fifteenx-ray images. In response to the initiating of the playing of the groupof x-ray images, the group of x-ray images may be sorted based on arespiratory order of the patient and each of the x-ray images may beprovided one after another in a progression so that each x-ray image maybe provided without further input from the user. Each of the x-rayimages may be provided for a predefined amount of time so that when thepredefined amount of time has elapsed, the subsequent x-ray image in thesorted respiratory order may be provided.

FIG. 10 illustrates the sorting 1000 of the group of images based onamplitude and phase associated with respiratory order of a patient. Ingeneral, the sorting 1000 may be based on x-ray images that havesatisfied correlation parameter thresholds and that have been selectedby a user to be used in a model to identify a targeted region of apatient.

As shown in FIG. 10, a first x-ray image 1010 may be in the group ofx-ray images used in the model. The first x-ray image 1010 may beidentified as being associated with a position or amplitude during anupwards phase of the respiratory motion of the patient. As such, thefirst x-ray image 1010 may be sorted to a position 1011. The secondx-ray image 1020 may be associated with a second position or amplitudeduring a downwards phase of the respiratory motion of the patient. Assuch, the second x-ray image 1020 may be sorted to a second position1021 based on the amplitude and downwards phase of its correspondinglocation. Similarly, the third x-ray image 1030 may be sorted to aposition 1031 and the fourth x-ray image 1040 may be sorted to aposition 1041, each based on the amplitude and respiratory phase of itscorresponding location. In some embodiments, the first x-ray image 1010may be captured or taken of a patient first (e.g., has the earliest ageas previously described), the second x-ray image 1020 may be takensecond, the third x-ray image 1030 may be taken third, and the fourthx-ray image 1040 may be taken fourth. Thus, the group of images may besorted so that when the movie mode or playing of the x-ray images isinitiated, the first x-ray image 1010 is provided first, followed by thethird x-ray image 1030, fourth x-ray image 1040, and then the thirdx-ray image 1030 as the movie mode may provide the x-ray images in asequence based on the sorted order as opposed to an age or when thex-ray images were taken.

Thus, the x-ray images 1010, 1020, 103, and 1040 may be sorted topositions within the respiratory order or motion of the patient. Forexample, the x-ray image 1010 may be sorted to a first position in therespiratory order and the x-ray image 1020 may be sorted to a finalposition in the respiratory order. Furthermore, the x-ray images 1030and 1040 may be sorted to intermediate positions in the respiratoryorder that are between the first position and the final position. Insome embodiments, the respiratory order may correspond to one period ofa waveform that represents the upwards motion of a patient during arespiratory motion and a downwards motion of the patient during therespiration motion. The x-ray image that is sorted to the earliest pointin the period may be the first location and the x-ray image that issorted the latest point in the period of the waveform may be the finallocation in the period that represents the respiratory order or motion.The x-ray images that are between the earliest and latest point may beconsidered to be intermediate points.

FIG. 11 is a flow diagram of an example method 100 to provide a group ofimages and identify a false positive image. In general, the method 1100may be performed by processing logic that may comprise hardware (e.g.,processing device, circuitry, dedicated logic, programmable logic,microcode, hardware of a device, integrated circuit, etc.), software(e.g., instructions run or executed on a processing device), or acombination thereof. In some embodiments, the method 1100 is providedvia the graphical user interface 700 or 800 of FIGS. 7 and 8.

As shown in FIG. 11, the method 1100 may begin with the processing logicreceiving an input to provide a group of images based on a sortedrespiratory order of a patient (block 1110). For example, a selection ofa GUI element as described with regard to FIG. 7 may be received. Inresponse to the input, images from the group of images may be providedin a progression that is based on the sorted respiratory order (block1120). The location of the tumor of the patient may be more easilyascertainable around healthy tissue and bone structure during theplaying of the images. As the images from the group of images areprovided and progressed through, the processing logic may identify oneof the images as a false positive during the playing of the images(block 1130). For example, a selection may be provided by a user via theGUI of FIG. 7 or 8 based on the contour and the tumor where the contourfor a particular image is out of an expected order for the image.Further details with regard to identifying a false positive during theproviding of the x-ray images in a sequence are described in conjunctionwith FIGS. 12A-C. In some embodiments, the false positive may beidentified when the contour for the particular image does not containthe tumor of the patient in the x-ray image. In the same or alternativeembodiments, an indication may be provided in the GUI when a falsepositive is identified. For example, a user may provide the indicationof the false positive. The processing logic may further receive aselection to pause the providing of the images in the sequence (block1140). For example, another selection may be provided via the GUI ofFIG. 7 or 8 to stop the playing of the x-ray images during the moviemode. Subsequently, the processing logic may receive a selection for theimage on which the playing of the group of images has stopped or beenpaused during the movie mode (block 1150). The selection may be toremove the image for which the false positive is identified from thegroup of images that are used by the model.

As such, a movie mode for the group of images may be provided so thatthe group of images are sorted based on a respiratory order and aresequentially played in a graphical user interface. The location andmovement of the tumor may be more easily ascertained by the playing ofthe group of images in the movie mode. Furthermore, the movement of thecontour and tracking the contour over the location of the tumor duringthe respiratory order of the patient may be identified so that if thecontour is out of an expected order, then the corresponding image may beidentified as a false positive and may be removed from use in the modelthat is used to identify the targeted region that will receive theradiation treatment.

FIG. 12A illustrates an example of a moving of the contour during theplaying of the group of images. In general, the moving of the contourmay be provided in response to a selection to initiate a movie mode fora group of images that are provided by a graphical user interface (e.g.,graphical user interface 700).

As shown in FIG. 12A, a series of contours may be provided where eachcontour represents a location of a contour that has been superimposed ona corresponding x-ray image. As the x-ray images are played in the moviemode, the contours for each x-ray image may move locations. For example,as shown, a first contour 1201 may be at a first location, a secondcontour 1202 may be at a second location, a third contour 1203 may be ata third location, and a fourth contour 1204 may be at a fourth location.The locations of the first through fourth contours 1201 through 1204 maybe in an expected placement or path. For example, the path may beapproximately a straight line, an oval, or other such geometric shape.As shown in FIG. 12A, the contours 1201-1204 are in a straight line. Assuch, an indication may be provided that there is not a false positivewith the contours or x-ray images and the model may be appropriate forproviding radiation treatment for the targeted region. In someembodiments, instead of a straight line, an indication that there is nota false positive may correspond to the contours 1201-1204 incrementallymoving within a particular motion pattern. For example, when thecontours 1201-1204 may move within a curve, an ellipse, or any otherlogical sequence or pattern in an incremental fashion, then there maynot be a false positive.

FIG. 12B illustrates an example of a moving of the contour during theplaying of the group of images that is associated with a false positive.In general, the moving of the contour may be provided in response to aselection to initiate a movie mode for a group of images that areprovided by a graphical user interface (e.g., graphical user interface700).

As shown in FIG. 12B, the series of contours may include a first contour1221 that is provided first in an x-ray image in movie mode, secondcontour 1222 that is provided second, third contour 1223 that isprovided third, and a fourth contour 1224 that is provided fourth.During the playing of the group of images with the superimposed contoursduring movie mode, the first contour 1221 to second contour 1222 may bein a particular direction (e.g., a downwards motion) and the fourthcontour 1224 may be further in the same direction (e.g., also in adownwards motion). Thus, each of the first contour 1221, second contour1222, and fourth contour 1224 may be in locations that correspond to amovement of the contour between x-ray images in the same direction.However, the third contour 1223 may be located at a position that is theopposite (e.g., in an upwards motion) from the first contour 1221,second contour 1222, and fourth contour 1224. For example, the locationof the third contour 1223 is after the location of the second contour1222 and before the location of the fourth contour 1224. As such, with ajump, or a discontinuous motion, associated with the third contour 1223,an identification may be provided that the x-ray image with thesuperimposed third contour 1223 is a false positive.

FIG. 12C illustrates another example of a moving of the contour duringthe playing of the group of images that is associated with a falsepositive. In general, the moving of the contour may be provided by agraphical user interface 700 of FIG. 7.

As shown in FIG. 12C, the series of contours may include a first contour1231, second contour 1232, third contour 1233, and a fourth contour1234. The first contour 1231, second contour 1232, and third contour1233 may be within a similar path (e.g., a line). However, as shown, thefourth contour 1234 may be outside of the expected path (e.g., not at alocation along the line). As such, an identification may be providedthat the x-ray image with the superimposed fourth contour 1234 is afalse positive.

FIG. 13 illustrates a system that may be used in generating a treatmentplan and the performing of radiation treatment. These systems may beused to perform, for example, the methods described above. As describedbelow and illustrated in FIG. 13, a system 1300 may include a diagnosticimaging system 1305, a treatment planning system 1310, the treatmentdelivery system 200 as described with regard to FIG. 2, and a motiondetecting system (not shown). In one embodiment, the diagnostic imagingsystem 1305 and the motion detecting system are combined into a singleunit.

Diagnostic imaging system 1305 may be any system capable of producingmedical diagnostic images of a patient that may be used for subsequentmedical diagnosis, treatment planning, treatment simulation and/ortreatment delivery. For example, diagnostic imaging system 1305 may be acomputed tomography (CT) system, a magnetic resonance imaging (MM)system, a positron emission tomography (PET) system, or the like. Forease of discussion, diagnostic imaging system 1305 may be discussedbelow at times in relation to an x-ray imaging modality. However, otherimaging modalities such as those above may also be used.

In one embodiment, diagnostic imaging system 1305 includes an imagingsource 1320 to generate an imaging beam (e.g., x-rays) and an imagingdetector 1330 to detect and receive the imaging beam generated byimaging source 1320.

The imaging source 1320 and the imaging detector 1330 may be coupled toa processing device 1325 to control the imaging operation and processimage data. In one embodiment, diagnostic imaging system 1305 mayreceive imaging commands from treatment delivery system 200.

Diagnostic imaging system 1305 includes a bus or other means 1380 fortransferring data and commands among processing device 1325, imagingsource 1320 and imaging detector 1330. Processing device 1325 mayinclude one or more general-purpose processors (e.g., a microprocessor),special purpose processor such as a digital signal processor (DSP) orother type of device such as a controller or field programmable gatearray (FPGA). Processing device 1325 may also include other components(not shown) such as memory, storage devices, network adapters and thelike. Processing device 1325 may be configured to generate digitaldiagnostic images in a standard format, such as the DICOM (DigitalImaging and Communications in Medicine) format, for example. In otherembodiments, processing device 1325 may generate other standard ornon-standard digital image formats. Processing device 1325 may transmitdiagnostic image files (e.g., the aforementioned DICOM formatted files)to treatment delivery system 200 over a data link 1383, which may be,for example, a direct link, a local area network (LAN) link or a widearea network (WAN) link such as the Internet. In addition, theinformation transferred between systems may either be pulled or pushedacross the communication medium connecting the systems, such as in aremote diagnosis or treatment planning configuration. In remotediagnosis or treatment planning, a user may utilize embodiments of thepresent disclosure to diagnose or treat a patient despite the existenceof a physical separation between the system user and the patient.

Treatment delivery system 200 includes a therapeutic and/or surgicalradiation source such as the LINAC 201 to administer a prescribedradiation dose to a target volume in conformance with a treatment plan.Treatment delivery system 200 may also include a processing device 1402to control radiation source 201, image-based aperture verificationsystem 1397, primary aperture verification system 1395, receive andprocess data from an imaging system 210, and control a patient supportdevice such as a treatment couch 206. Alternatively or additionally,image-based aperture verification system 1397 may include its ownprocessing device, which may perform operations described herein.Processing device 1402 may be configured to register 2D radiographicimages received from diagnostic imaging system 1305, from one or moreprojections, with digitally reconstructed radiographs (DRRs) generatedby processing device 1325 in diagnostic imaging system 1305 and/or DRRsgenerated by processing device 1340 in treatment planning system 1310.Processing device 1402 may include one or more general-purposeprocessors (e.g., a microprocessor), special purpose processor such as adigital signal processor (DSP) or other type of device such as acontroller or field programmable gate array (FPGA). Similarly, aprocessing device of image-based aperture verification system 1397 mayinclude one or more general-purpose processors (e.g., a microprocessor),special purpose processor such as a digital signal processor (DSP) orother type of device such as a controller or field programmable gatearray (FPGA). Processing device 1402 and/or image based apertureverification system 1397 may also include other components (not shown)such as memory, storage devices, network adapters and the like.

In one embodiment, processing device 1402 includes system memory thatmay include a random access memory (RAM), or other dynamic storagedevices, coupled to a processing device, for storing information andinstructions to be executed by the processing device. The system memoryalso may be used for storing temporary variables or other intermediateinformation during execution of instructions by the processing device.The system memory may also include a read only memory (ROM) and/or otherstatic storage device for storing static information and instructionsfor the processing device.

Processing device 1402 may also be associated with a storage device,representing one or more storage devices (e.g., a magnetic disk drive oroptical disk drive) for storing information and instructions. Thestorage device may be used for storing instructions for performing thetreatment delivery steps discussed herein. Processing device 1402 may becoupled to radiation source 201 and treatment couch 206 by a bus 1392 orother type of control and communication interface.

Processing device 1402 may implement methods to manage timing ofdiagnostic x-ray imaging in order to maintain alignment of a target witha radiation treatment beam delivered by the radiation source 201.

In one embodiment, the treatment delivery system 200 includes an inputdevice 1378 and a display 1377 connected with processing device 1402 viabus 1392. The display 1377 may provide the GUIs 700 and/or 800. Thedisplay 1377 can also show trend data that identifies a rate of targetmovement (e.g., a rate of movement of a target volume that is undertreatment). The display can also show a current radiation exposure of apatient and a projected radiation exposure for the patient. The inputdevice 1378 can enable a clinician to adjust parameters of a treatmentdelivery plan during treatment.

Treatment planning system 1310 includes a processing device 1340 togenerate and modify treatment plans and/or simulation plans. Processingdevice 1340 may represent one or more general-purpose processors (e.g.,a microprocessor), special purpose processor such as a digital signalprocessor (DSP) or other type of device such as a controller or fieldprogrammable gate array (FPGA). Processing device 1340 may be configuredto execute instructions for performing treatment planning operations.

Treatment planning system 1310 may also include system memory 1335 thatmay include a random access memory (RAM), or other dynamic storagedevices, coupled to processing device 1340 by bus 1386, for storinginformation and instructions to be executed by processing device 1340.System memory 1335 also may be used for storing temporary variables orother intermediate information during execution of instructions byprocessing device 1340. System memory 1335 may also include a read onlymemory (ROM) and/or other static storage device coupled to bus 1386 forstoring static information and instructions for processing device 1340.

Treatment planning system 1310 may also include storage 1345,representing one or more storage devices (e.g., a magnetic disk drive oroptical disk drive) coupled to bus 1386 for storing information andinstructions. Storage 1345 may be used for storing instructions forperforming treatment planning.

Processing device 1340 may also be coupled to a display device 1350,such as a cathode ray tube (CRT) or liquid crystal display (LCD), fordisplaying information (e.g., a 2D or 3D representation of a volume ofinterest (VOI)) to a user. An input device 1355, such as a keyboard, maybe coupled to processing device 1340 for communicating informationand/or command selections to processing device 1340. One or more otheruser input devices (e.g., a mouse, a trackball or cursor direction keys)may also be used to communicate directional information, to selectcommands for processing device 1340 and to control cursor movements ondisplay 1350.

Treatment planning system 1310 may share its database (e.g., data storedin storage 1345) with a treatment delivery system, such as treatmentdelivery system 200, so that it may not be necessary to export from thetreatment planning system prior to treatment delivery. Treatmentplanning system 1310 may be linked to treatment delivery system 200 viaa data link 1390, which may be a direct link, a LAN link or a WAN link.

It should be noted that when data links 1383 and 1390 are implemented asLAN or WAN connections, any of diagnostic imaging system 1305, treatmentplanning system 1310 and/or treatment delivery system 200 may be indecentralized locations such that the systems may be physically remotefrom each other. Alternatively, any of diagnostic imaging system 1305,treatment planning system 1310, and/or treatment delivery system 200 maybe integrated with each other in one or more systems.

FIG. 14 illustrates an example machine of a computer system 1400 withinwhich a set of instructions, for causing the machine to perform any oneor more of the methodologies discussed herein, may be executed. Inalternative implementations, the machine may be connected (e.g.,networked) to other machines in a LAN, an intranet, an extranet, and/orthe Internet. The machine may operate in the capacity of a server or aclient machine in client-server network environment, as a peer machinein a peer-to-peer (or distributed) network environment, or as a serveror a client machine in a cloud computing infrastructure or environment.

The machine may be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, a switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The example computer system 1400 includes a processing device 1402, amain memory 1404 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM) or RambusDRAM (RDRAM), etc.), a static memory 1406 (e.g., flash memory, staticrandom access memory (SRAM), etc.), and a data storage device 1418,which communicate with each other via a bus 1430.

Processing device 1402 represents one or more general-purpose processingdevices such as a microprocessor, a central processing unit, or thelike. More particularly, the processing device may be complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or processor implementing other instruction sets, orprocessors implementing a combination of instruction sets. Processingdevice 1402 may also be one or more special-purpose processing devicessuch as an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 1402 is configuredto execute instructions 1426 for performing the operations and stepsdiscussed herein.

The computer system 1400 may further include a network interface device1408 to communicate over the network 1420. The computer system 1400 alsomay include a video display unit 1410 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)), an alphanumeric input device 1412(e.g., a keyboard), a cursor control device 1414 (e.g., a mouse), agraphics processing unit 1422, a signal generation device 1416 (e.g., aspeaker), graphics processing unit 1422, video processing unit 1428, andaudio processing unit 1432.

The data storage device 1418 may include a machine-readable storagemedium 1424 (also known as a computer-readable medium) on which isstored one or more sets of instructions or software 1426 embodying anyone or more of the methodologies or functions described herein. Theinstructions 1426 may also reside, completely or at least partially,within the main memory 1404 and/or within the processing device 1402during execution thereof by the computer system 1400, the main memory1404 and the processing device 1402 also constituting machine-readablestorage media.

In one implementation, the instructions 1426 include instructions for amodel controller 499 to implement functionality corresponding to thedisclosure herein. While the machine-readable storage medium 1424 isshown in an example implementation to be a single medium, the term“machine-readable storage medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “machine-readable storage medium” shall also betaken to include any medium that is capable of storing or encoding a setof instructions for execution by the machine and that cause the machineto perform any one or more of the methodologies of the presentdisclosure. The term “machine-readable storage medium” shall accordinglybe taken to include, but not be limited to, solid-state memories,optical media and magnetic media.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as “identifying” or “determining” or “executing” or“performing” or “collecting” or “creating” or “sending” or the like,refer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage devices.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for theintended purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions, each coupled to a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the method. The structure for a variety of thesesystems will appear as set forth in the description below. In addition,the present disclosure is not described with reference to any particularprogramming language. It will be appreciated that a variety ofprogramming languages may be used to implement the teachings of thedisclosure as described herein.

The present disclosure may be provided as a computer program product, orsoftware, that may include a machine-readable medium having storedthereon instructions, which may be used to program a computer system (orother electronic devices) to perform a process according to the presentdisclosure. A machine-readable medium includes any mechanism for storinginformation in a form readable by a machine (e.g., a computer). Forexample, a machine-readable (e.g., computer-readable) medium includes amachine (e.g., a computer) readable storage medium such as a read onlymemory (“ROM”), random access memory (“RAM”), magnetic disk storagemedia, optical storage media, flash memory devices, etc.

In the foregoing disclosure, implementations of the disclosure have beendescribed with reference to specific example implementations thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of implementations of thedisclosure as set forth in the following claims. The disclosure anddrawings are, accordingly, to be regarded in an illustrative senserather than a restrictive sense.

What is claimed is:
 1. A method comprising: identifying one of moreimages of a plurality of images that are used with a previouslygenerated model associated with a position and motion of a targetedregion of a patient to receive radiation treatment; generating, by aprocessing device, a new model to be associated with the position andmotion of the targeted region based on a selection that is associatedwith one of the one or more images of the plurality of images, whereinthe new model is a relationship between a series of internal featuresand external marker positions; and delivering radiation to the targetedregion based on the new model.
 2. The method of claim 1, furthercomprising sorting the one or more images of the plurality of images. 3.The method of claim 1, wherein the one of the one or more images of theplurality images is at an intermediate position in a respiratory order.4. The method of claim 2, further comprising receiving the selectionassociated with one or more images of the plurality of images.
 5. Themethod of claim 4, wherein the sorting of the one or more images of theplurality of images is based on a respiratory order associated with thepatient, and wherein the selection corresponds to a removal of one imagefrom being used with the new model that is associated with the positionand motion of the targeted region, wherein the generation of the newmodel is not based on the removed one image.
 6. The method of claim 1,wherein the selection corresponds to a removal of one or more imagesfrom being used with the new model that is associated with the positionand motion of the targeted region, wherein the generation of the newmodel is not based on the removed one or more images.
 7. The method ofclaim 4, wherein the selection corresponds to a changing of acorrelation output parameter threshold of a target tracking processassociated with the new model.
 8. A system comprising: a memory to storea new model; and a processing device operatively coupled with the memoryto: identify one of more images of a plurality of images that are usedwith a previously generated model associated with a position and motionof a targeted region of a patient to receive radiation treatment;generate the new model to be associated with the position and motion ofthe targeted region based on a selection that is associated with one ofthe one or more images of the plurality of images, wherein the new modelis a relationship between a series of internal features and externalmarker positions; and deliver radiation to the targeted region based onthe new model.
 9. The system of claim 8, wherein the processing devicefurther to sort the one or more images of the plurality of images. 10.The system of claim 8, wherein the one of the one or more images of theplurality images is at an intermediate position in a respiratory order.11. The system of claim 10, wherein the sorting of the one or moreimages of the plurality of images is based on a respiratory orderassociated with the patient, and wherein the selection corresponds to aremoval of one image from being used with the new model that isassociated with the position and motion of the targeted region, whereinthe generation of the new model is not based on the removed one image.12. The system of claim 10, wherein the selection corresponds to achanging of a correlation output parameter threshold of a targettracking process associated with the new model.
 13. The system of claim8, wherein the selection corresponds to a removal of one or more imagesfrom being used with the new model that is associated with the positionand motion of the targeted region, wherein the generation of the newmodel is not based on the removed one or more images.
 14. Anon-transitory computer readable medium comprising instructions that,when executed by a processing device, cause the processing device to:identify one of more images of a plurality of images that are used witha previously generated model associated with a position and motion of atargeted region of a patient to receive radiation treatment; generate,by the processing device, a new model to be associated with the positionand motion of the targeted region based on a selection that isassociated with one of the one or more images of the plurality ofimages, wherein the new model is a relationship between a series ofinternal features and external marker positions; and deliver radiationto the targeted region based on the new model.
 15. The non-transitorycomputer readable medium of claim 14, wherein the processing devicefurther to sort the one or more images of the plurality of images. 16.The non-transitory computer readable medium of claim 14, wherein the oneof the one or more images of the plurality images is at an intermediateposition in a respiratory order.
 17. The non-transitory computerreadable medium of claim 15, wherein the processing device is further toreceive the selection associated with one or more images of theplurality of images.
 18. The non-transitory computer readable medium ofclaim 17, wherein the sort of the one or more images of the plurality ofimages is based on a respiratory order associated with the patient, andwherein the selection corresponds to a removal of one image from beingused with the new model that is associated with the position and motionof the targeted region, wherein the generation of the new model is notbased on the removed one image.
 19. The non-transitory computer readablemedium of claim 17, wherein the selection corresponds to a changing of acorrelation output parameter threshold of a target tracking processassociated with the new model.
 20. The non-transitory computer readablemedium of claim 14, wherein the selection corresponds to a removal ofone or more images from being used with the new model that is associatedwith the position and motion of the targeted region, wherein thegeneration of the new model is not based on the removed one or moreimages.